Random survival forests

Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone, Michael S. Lauer

Research output: Contribution to journalArticle

545 Scopus citations

Abstract

We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, random Survival Forest.

Original languageEnglish (US)
Pages (from-to)841-860
Number of pages20
JournalAnnals of Applied Statistics
Volume2
Issue number3
DOIs
StatePublished - Sep 1 2008

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Keywords

  • Conservation of events
  • Cumulative hazard function
  • Ensemble
  • Out-of-bag
  • Prediction error
  • Survival tree

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

Cite this

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. Annals of Applied Statistics, 2(3), 841-860. https://doi.org/10.1214/08-AOAS169